Predicting edges using graph theory is known in the art, and, interest in this field has increased in the recent past, motivated by different businesses, such as networking, including, for example, telecommunications and social networking. Prior art graph theory techniques are built under the same hypothesis: the graph is always growing or, stated differently, the model assumes a cumulative graph through time. That is, the edges and nodes existing at time T0 will always exist in future instances of the graph. Such a graph becomes densely populated over time and hence difficult to analyze. A further disadvantage is that information about a volatile edge may be lost, or the algorithm has to maintain different data structures to store such information.
In general terms, the prior art can be separated into two parts: edge prediction, and models for evolving graphs. The former describes techniques regarding predicting edges in a graph, while the latter describes only different models or data-structures to capture the evolving graphs.